Phase-Dependent Collective Contrast in Finite Open Dicke Systems as a Seed for Engineering Binding in Digital Spiking Networks: Persistent Unified States, Refusal Protection, and Ultra-Sparse Attractors at 0.0417 spikes/ms

This work bridges quantum optics and neuromorphic engineering to provide a 13-step computational proof-of-concept for Phase-Dependent Collective Contrast. We demonstrate that the 1. 91x Critical Scaling Gap—originally identified in the dissipative Dicke model—serves as a robust stability anchor for engineering ultra-sparse, "Elite" spiking neural networks. Using the Nengo neuromorphic framework, we translate quantum steady-state ratios into a neural "Refusal Ratio" (R₄₅₅), achieving stable binding attractors at an ultra-low firing rate of 0. 0417 spikes/ms. This repository contains the complete "Hu Tao" protocol, ranging from QuTiP-based quantum simulations to autonomous neuromorphic motor control (Ping Pong) and quantum-seeded neural crossover. Key Highlights: Theoretical Foundation: Grounded in the Phase-Dependent Scaling Program (2026) and the gc = 6/pi convergence law. Neuromorphic Implementation: Successful mapping of the 1. 912601 stability constant to neural refusal gates. Data Included: 13 sequential Jupyter Notebooks documenting the full discovery pipeline from "Duel" simulations to "Quantum-Seeded Birth. " Related Works: This manuscript is theoretically grounded in the 1. 91x Critical Scaling Gap identified in Scaling Behavior in Noisy Dicke Model Simulations (Omandac 2026, Paper 1).